EnsArtNet: Ensemble neural network architecture for identifying art styles from paintings
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0196477" target="_blank" >RIV/00216305:26220/26:0196477 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1296207425000056" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1296207425000056</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.culher.2025.01.005" target="_blank" >10.1016/j.culher.2025.01.005</a>
Alternative languages
Result language
angličtina
Original language name
EnsArtNet: Ensemble neural network architecture for identifying art styles from paintings
Original language description
The digitization of paintings offers many benefits and opportunities for artists, collectors, and the public. It opens possibilities for researchers to investigate new hidden patterns that were not obvious to experts before. This work aims to develop a methodology that can identify and compare painting styles from various famous painters, such as Vincent van Gogh, Pablo Picasso, Claude Monet, and others, using an ensemble convolutional neural network (CNN). Our approach, named EnsArtNet, can distinguish between the styles of the artists' paintings with high accuracy and objectively measure the similarity with the other artists' styles. The proposed model was compared to several other state-of-the-art neural network architectures, and we show that EnsArtNet performs better than the compared one. Our model gives promising accuracy on two large-scale datasets: 84.93% on the WikiArt dataset and 86.65% on the Best Artworks of All Time dataset, which is better by more than 6% compared to other evaluated architectures. In this work, we also showed that a complex neural network architecture is efficient in this field of research, and an explanation using the GradCAM method supported it. Our methodology can help art researchers and enthusiasts analyze paintings' stylistic features and similarities and appreciate the creativity and diversity of visual arts. (c) 2025 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
<a href="/en/project/VK01010107" target="_blank" >VK01010107: Application of artificial intelligence for forensic identification of soil phases</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
JOURNAL OF CULTURAL HERITAGE
ISSN
1296-2074
e-ISSN
1778-3674
Volume of the periodical
72
Issue of the periodical within the volume
2025
Country of publishing house
FR - FRANCE
Number of pages
10
Pages from-to
71-80
UT code for WoS article
001419521400001
EID of the result in the Scopus database
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